Clustering-based spatial transfer learning for short-term ozone forecasting

Journal Article (2022)
Author(s)

T. Deng (TU Delft - Mathematical Physics)

Astrid Manders (TNO)

J. Jin (TU Delft - Mathematical Physics, Nanjing University of Information Science and Technology)

Hai Xiang Lin (TU Delft - Mathematical Physics, Universiteit Leiden)

Research Group
Mathematical Physics
DOI related publication
https://doi.org/10.1016/j.hazadv.2022.100168
More Info
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Publication Year
2022
Language
English
Research Group
Mathematical Physics
Volume number
8
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Abstract

Ground-level ozone is a critical atmospheric pollutant, and high concentrations of ozone can damage human health, affect plant growth and cause ecological harm. Traditional chemical transport models and popular machine learning models have difficulty in predicting ozone concentrations, especially in times with high concentrations. We proposes a clustering-based spatial transfer learning Multilayer Perceptron (SPTL-MLP) to predict ozone concentration at the target observation station for the next three days. We use k-means clustering algorithm to find similar stations and train them together to get a base model for spatial transfer learning. For practical applications, a weighted loss function has been designed with an extra emphasis on reducing prediction errors of high ozone concentrations. Evaluation using historical data of stations in Germany shows that our SPTL-MLP model has a smaller error (reduced by 9.13%) and higher prediction accuracies of ozone exceedances (improved by 8.21% and 16.9%) compared to MLP (without spatial transfer). The results demonstrate the effectiveness of the SPTL-MLP in the short-term ozone forecast. It can be used for timely warning of ozone exceedances and help governments to detect air quality.